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The iCub Humanoid Robot:An Open-Systems Platform for Research in Cognitive DevelopmentGiorgio Mettaa,Lorenzo Natalea,Francesco Noria,Giulio Sandinia,David Vernona,Luciano Fadigaa,b,Claes von Hofstenc,Jos´e Santos-Victord,Alexandre Bernardinod,Luis MontesanodaItalian Institute of Technology (IIT),Italy.bUniversity of Ferrara,Italy.cUniversity of Uppsala,Sweden.dInstituto Superior T´ecnico,Portugal.AbstractWe describe a humanoid robot platform—the iCub —which was designed to support collaborative researchin cognitive development through autonomous exploration and social interaction.The motivation for thiseﬀort is the conviction that signiﬁcantly greater impact can be leveraged by adopting an open systemspolicy for software and hardware development.This creates the need for a robust humanoid robot thatoﬀers rich perceptuo-motor capabilities with many degrees of freedom,a cognitive capacity for learning anddevelopment,a software architecture that encourages reuse & easy integration,and a support infrastucturethat fosters collaboration and sharing of resources.The iCub satisﬁes all of these needs in the guise of anopen-system platform which is freely available and which has attracted a growing community of users anddevelopers.To date,twenty iCubs each comprising approximately 5000 mechanical and electrical parts havebeen delivered to several research labs in Europe and to one in the U.S.A.Keywords:Open-source humanoid robot,cognition,development,learning,phylogeny,ontogeny1.IntroductionRobotics,by deﬁnition,takes inspiration from na-ture and the humanoid concept is perhaps the bestexample.When we consider the possibility of creat-ing an artefact that acts in the world,we face a pre-liminary and fundamental choice:eﬃciency (achievedby being task-speciﬁc) or versatility (achieved bybiologically-compatibility development).The ﬁrstoption leads to the realization of automatic systemsthat are very fast and precise in their operations.The limitations of automatic systems are purely tech-nological ones (e.g.miniaturization).The secondoption is what we consider to be a humanoid:abiological-like system which takes decisions and actsin the environment,which adapts and learns how tobehave in new situations,and which invents new so-lutions on the basis of the past experience.The fasci-nating aspect of the humanoid is the possibility to in-teract with it:to teach,to demonstrate,even to com-municate.It should be stressed that the attempt toadopt the strategy of ‘biological compatibility’ doesnot represent an intellectual exercise but is promptedby the idea that a humanoid interacting with humanbeings must share with them representations,motorbehaviours and perhaps,even kinematics and degreesof freedom.To interact,a humanoid must ﬁrst act (and notsimply move),perceive,categorize and therefore,un-derstand.These capabilities cannot arise from pre-Preprint submitted to Neural Networks June 19,2010Figure 1:The iCub humanoid robot:an open-systems plat-form for research in cognitive development.compiled software routines.On the contrary,theyrealize themselves through an ontogenetic pathway,simulating what happens in developing infants.Inother words,humanoids must act in the environmentto know it.It should be stressed that ‘to know theenvironment’ does not mean to categorize an assem-bly of static structures and objects but requires,asan essential requisite,to understand the consequencesof generated actions (e.g.a glass breaks when fallson the ground).During this knowledge acquisition,attempts and errors are fundamental because they in-crease the ﬁeld of exploration.This is the main diﬀer-ence between a humanoid and an automatic system:for the latter,errors are not allowed by deﬁnition.The developmental process leading to a maturehumanoid requires a continuous study of its hu-man counterpart.This study only partially over-laps with traditional neuroscience,because of its pe-culiar interdisciplinarity.In other words,the syn-ergy between neuroscience (particularly neurophysi-ology) and robotics,gives rise to a new discipline inwhich bi-directional beneﬁts are expected.In fact,this knowledge sharing rewards not only roboticsbut also neuroscience since the developing (learning)humanoid forms a behaving model to test neuro-scientiﬁc hypotheses by simplifying some extremelycomplex problems.Particularly,it allows what is notconceivable in human neuroscience:to investigate theeﬀects of experimental manipulations on developmen-tal processes.This opens up vast new opportunitiesfor advancing our understanding of humans and hu-manoids.This paper describes the development of theiCub humanoid robot (see Figure 1) and our eﬀortsto navigate this unchartered territory,aided by a con-stantly growing community of iCub users and devel-opers.The iCub is a 53 degree-of-freedom humanoidrobot of the same size as a three or four year-oldchild.It can crawl on all fours and sit up.Its handsallow dexterous manipulation and its head and eyesare fully articulated.It has visual,vestibular,audi-tory,and haptic sensory capabilities.The iCub is anopen systems platform:researchers can use it andcustomize it freely since both hardware and softwareare licensed under the GNU General Public Licence(GPL).1The iCub design is based on a roadmap of humandevelopment [1] (see Section 3).This description ofhuman development stresses the role of predictioninto the skilful control of movement:development isin a sense the gradual maturation of predictive capa-bilities.It adopts a model of “sensorimotor” controland development which considers “action” (that is,movements with a goal,generated by a motivatedagent which are predictive in nature) as the basicelement of cognitive behaviours.Experiments withinfants and adults have shown that the brain is notmade of a set of isolated areas dealing with percep-tion or motor control but rather that multisensoryneurons are the norm.Experiments have proven theinvolvement of the motor system,including the ar-ticulation of speech,in the ﬁne perception of themovements of others.The iCub employs a com-1the iCub software and hardware are licensed under theGNU General Public Licence (GPL) and GNU Free Documen-tation Licence (FDL),respectively.2putational model of aﬀordances which includes thepossibility of learning both the structure of depen-dences between sets of random variables (e.g.per-ceptual qualities vs.action and results),their eﬀec-tive links and their use in deciding how to controlthe robot.Aﬀordances form the quintessential prim-itives of cognition by mixing perception and actionin a single concept or representation.It builds ona computational model of imitation and interactionbetween humans and robots by evaluating the auto-matic construction of models from experience (e.g.trajectories),their correction via feedback,timingand synchronization.This explores the domain be-tween mere sensorimotor associations and the possi-bility of true communication between robot and peo-ple.The iCub involved the design from scratch of acomplete humanoid robot including mechanics,elec-tronics (controllers,I/O cards,buses,etc.) and therelated ﬁrmware and it adopted and enhanced open-systems middleware (YARP) [2].Finally,it has re-sulted in the creation of a community of active usersand researchers working on testing,debugging,andimproving the iCub of the future.2.Design GoalsThe design of the iCub started fromthe considera-tion that the construction of cognitive systems couldnot progress without a certain number of ingredients:the development of a sound formal understanding ofcognition [3],the study of natural cognition and,par-ticularly important,the study of the development ofcognition [4,5],the study of action in humans byusing neuroscience methods [6,7],and the physicalinstantiation of these models in a behaving humanoidrobot [8,9].Our research agenda starts from cognitive neuro-science research and proceeds by addressing,for ex-ample,the role of manipulation as a source of knowl-edge and new experience,as a way to communicatesocially,as a tool to teach and learn,or as a meansto explore and control the environment.We wouldlike to stress here that collaboration between neuro-science,computer science,and robotics is truly in-tended as bi-directional.On one side,the iCub cog-nitive architecture is a system as much as possible“biologically oriented”.2On the other side,real bio-logical systems were examined according to problemsthat we deemed important for elucidating the role ofcertain behaviours or brain regions in a larger pictureof the brain.Examples of this research are:the abil-ity to grasp unknown objects on the basis of theirshape and position with one and two hands,to as-semble simple objects with plugs,and to coordinatethe use of two hands (e.g.parts mating,handling ofsoft materials).These abilities require visuo-hapticobject recognition and multimodal property trans-fer,visual recognition of the body gestures of others,imitation of one and two-hand gestures,and com-munication and interaction through body and handgestures.Ano-less-important scientiﬁc objective is the studyof of the initial period of human cognitive develop-ment and its implementation on the iCub.Ourworking method is,in fact,not to pre-program thecognitive skills outlined earlier but,similarly to whathappens in humans,to implement them into a sys-tem that can learn much like a human baby does.We understand aspects of human development andcan make speciﬁc informed choices in building an ar-tiﬁcial adaptable system.For example,developmen-tal science now points out at how much action,per-ception and cognition are tightly coupled in develop-ment.This means that cognition cannot be studiedwithout considering action and embodiment and howperception and cognition are intertwined into devel-opment [7].Exemplar experimental scenarios are dis-covering the action possibilities of the body (the socalled body map),learning to control one’s upper andlower body (crawling,bending the torso) to reach fortargets,learning to reach static and moving targets,and learning to balance in order to perform stable2It is important to note that biological plausibility or simi-larity in the iCub is not intended as a faithful implementationof neural simulations to a very detailed level.We don’t thinkthat this approach is feasible given the available hardware.The digital computer is not the brain and it would be waste-ful to try to use computers in this sense.On the other hand,the gross features of the architecture are biologically plausibleby including attention,memory (procedural and declarative),reaching,grasping,action selection,and aﬀective state.3object manipulations when crawling or sitting.Theyinclude also discovering and representing the shapeof objects and discovering and representing object af-fordances (e.g.the use of “tools”).Interaction withother agents is also important:recognizing manipula-tion abilities of others and relating those to one’s ownmanipulation abilities,learning to interpret and pre-dict the gestures of others,learning new motor skillsand new object aﬀordances by imitating manipula-tion tasks performed by others,learning what to im-itate and when to imitate others gestures,and learn-ing regulating interaction dynamics.Clearly,this isand ambitious research programme and it is far frombeing completed.However,we have set the basis fora solid development in this direction by providing theplatform and by setting up the whole infrastructure(together with examples and large parts of this set ofbehaviours).To enable the investigation of relevant cognitive as-pects of manipulation the design was aimed at max-imizing the number of degrees of freedom (DOF) ofthe upper part of the body (head,torso,arms,andhands).The lower body (legs) were initially designedto support crawling “on four legs” and sitting on theground in a stable position (and smoothly transitionfrom crawling to sitting).A recent study and conse-quent modiﬁcation of the legs allows bipedal walking,although this is still theoretical since the control soft-ware has not been developed yet.We are also design-ing a mobile base (on wheels) for the iCub which willallow mobility and autonomy (on battery).Mobility,in general,whether on wheels or by crawling,allowsthe robot to explore the environment and to graspand manipulate objects on the ﬂoor.The size of theiCub is that of a three- to four-year-old child andthe total number of degrees of freedom for the upperbody is 41 (7 for each arm,9 for each hand,6 for thehead and 3 for the torso and spine).The sensory sys-tem includes binocular vision,touch,binaural audi-tion and inertial sensors.Functionally,the iCub cancoordinate the movement of the eyes & hands,graspand manipulate lightweight objects of reasonable sizeand appearance,crawl on four legs and sit [10,11].Such a tool did not exist prior to the constructionof the iCub even considering the humanoid roboticproducts developed recently by Japanese companies(e.g.Sony,Honda,etc.) and it is still the only com-plete open-systems humanoid robot available today.To emphasize again,the design of the iCub placesstrong emphasis on manipulation since neural sciencetells us a story —a summary can be found in [12] —in which manipulation is central to human cognition.In fact,manipulation is the way through which weget to grips with the world,with the concept of ob-jecthood,with the social environment,and further,if we subscribe to this story,communication to thelevel of language evolved out of a process of adap-tation of the manual system into the one that con-trols speech.Equally important,the iCub has legsfor crawling which give the robot a chance for build-ing its own experience by exploring the environment,fetching objects,etc.This raises a whole new set ofissues since the robot has to link the frame of refer-ence of its perceptual abilities to a moving environ-ment rather than to the usual ﬁxed one as in manystationary platforms.One example is in building theunderstanding of the limits of the robots own body:in this case,the robot can exploit the fact that itsbody is relatively constant over time while the envi-ronment has a higher variability.A high variabilityin the environment helps in building this importantdistinction.3.Foundations of Human DevelopmentOur goal in studying the development of early cog-nition in humans is to model the relevant aspects ofsuch a process within the boundaries of an artiﬁcialsystem.In particular,we investigate the timeframeof a developmental process that begins to guide ac-tion by internal representations of upcoming events,by the knowledge of the rules and regularities of theworld,and by the ability to separate means and end(or cause and eﬀect).We study and model howyoung children learn procedures to accomplish goals,how they learn new concepts,and how they learn toimprove plans of actions.This research is stronglydriven by studies of developmental psychology andcognitive neuroscience and it has resulted in a physi-cal implementation on the iCub as well as a roadmapfor the development of cognitive abilities in humanoid4robots [1].To a large extent,this roadmap is a con-ceptual framework that forms the foundation of theiCub project.It surveys what is known about cogni-tion in natural systems,particularly from the devel-opmental standpoint,with the goal of identifying themost appropriate system phylogeny and ontogeny.Itexplored neuro-physiological and psychological mod-els of some of these capabilities,noting where ap-propriate architectural considerations such as sub-system interdependencies that might shed light onthe overall system organization.It uses the phy-logeny and ontogeny of natural systems to deﬁne theinnate skills with which the iCub must be equippedso that it is capable of ontogenetic development,todeﬁne the ontogenetic process itself,and to show ex-actly how the iCub should be trained or to what envi-ronments it should be exposed in order to accomplishthis ontogenetic development.Finally,it embracesthe creation and implementation of an architecturefor cognition:a computational framework for the op-erational integration of the distinct capabilities andcognitive skills developed in the project (these will bediscussed in the following sections).The iCub project takes an enactive approach tothe study of cognition whereby a cognitive systemdevelops it own understanding of the world aroundit through its interactions with the environment[13,14,15,16,17,18,19,20] and for which onto-genetic development is the only possible solution tothe acquisition of epistemic knowledge (the systemsrepresentations).In the enactive approach,cogni-tion is self-organizing and dynamical and correspondsto the acquisition (and development) of anticipatoryabilities and the development of a increasing space ofinteraction between the cognitive agent and its envi-ronment.We take this approach also in interpretingcognition in biological systems.Consequently,thenext important question is about the principles thatgovern the ontogenetic development of biological or-ganisms (e.g.as in [7]).Converging evidence fromvarious disciplines including developmental psychol-ogy and neuroscience is showing that behaviour inbiological organisms is organized in primitives thatwe can call actions (as distinct from movements orreactions).Actions are behaviours initiated by a mo-tivated subject,deﬁned by goals and guided usingprospective information (prediction).Elementary be-haviours are thus not reﬂexes but actions with goals,where perception and movement are integrated,andthey are initiated because of a motivation and thatare guided by and through prediction [7].To make this more operational and provide a de-scription of human development,we have to considerthree basic elements:1.What is innate,where do we start from?2.What drives development?3.How is new knowledge incorporated,i.e.whatare the forces that drive development?In looking at the ﬁrst question,developmental psy-chologists,typically refer to innate elements in termsof prenatal prestructuring or the so-called core abil-ities.Neither is to be imagined like a rigid deter-mination of perception-action couplings but rather ameans to facilitate development.Examples can befound in the prestructuring of the morphology of thebody,in the perceptual,and in the motor systems.The motor system requires constraints in order toreduce the large number of eﬀective degrees of free-dom and these constraints come in the form of mus-cular synergies.That is,to facilitate control,the ac-tivation of muscles is therefore organized into func-tional synergies at the beginning of life (and they areprobably formed already prenatally [21]).Similarly,perceptual structuring begins early in ontogenesis byrelying on the interaction between genetic and self-activity factors [22,23,24].In addition to these,prestructuring comes also in the form of speciﬁc coreabilities.Spelke [25] is one of the proponents of thisview.She discusses various aspects that show pre-structuring,such as the perception of objects andthe way they move,the perception of geometric rela-tionships and numerosities,and the understanding ofpersons and their actions.An important part of thecore knowledge has to do with people.Knowing the initial state of the system is only theﬁrst step.A model of human development then re-quires establishing what causes it.Motivations comein diﬀerent forms in the newborn:social and explo-rative.The social motive is what puts the infant inthe broader context of other human beings,thus pro-viding further possibilities for learning,safety,com-5fort,etc.Communication and language also developwithin the context of social interaction [26].The third basic element of this summary of humandevelopment is to show how new knowledge is ac-quired and incorporated.The brain is only one sideof this process:without interaction with the envi-ronment it would be of little use.Undoubtedly,thebrain has its own dynamics (proliferation of neurons,maps formation,migration,etc.) but the ﬁnal prod-uct is shaped by the dynamical interaction with theenvironment.Factors like exposure or deprivation tothe environment,the body biomechanics and bodygrowth are all fundamental to the development ofcognition.For instance,the appearance of reachingdepends critically on the appearance of 3D percep-tion through binocular disparity,on the emergence ofpostural control (and muscle strength),on the sepa-ration of the extension-ﬂexion synergies in the armand hand,on the perception of external motion,con-trol of the eyes for tracking and so forth.This is tosay that no single factor determines the appearance ofa particular new behaviour and it is therefore impor-tant to model complete systems in order to analyzeeven relatively simple cognitive behaviours.Complementary to developmental studies,neuro-physiology is also helping to show the inextricablycomplexity of the brain.Tantalizing results fromneuroscience are shedding light on the mixed motorand sensory representations used by the brain dur-ing reaching,grasping,and object manipulation.Wenow know a great deal about what happens in thebrain during these activities,but not necessarily why.Is the integration we see functionally important,orjust a reﬂection of evolution’s lack of enthusiasm forsharp modularity?A useful concept to help under-stand how such capabilities could develop is the well-known theory of Ungerleider and Mishkin [27] whoﬁrst formulated the hypothesis that the brain’s vi-sual pathways split into two main streams:the dorsaland the ventral.The dorsal is the so-called “where”pathway,concerned with the analysis of the spatialaspects of motor control.The ventral is related withthe “what”,that is,the identity of the targets ofaction.Milner and Goodale [28] reﬁned the theoryby proposing that objects are represented diﬀerentlyduring action than they are for a purely perceptualtask.The dorsal deals with the information requiredfor action,whereas the ventral is important for morecognitive tasks such as maintaining an object’s iden-tity and constancy.Although the dorsal/ventral seg-regation is emphasized by many commentators,it issigniﬁcant that there is a great deal of cross talk be-tween the streams [29].Among the arguments in favour of the ‘pragmatic’role of the visual information processed in the dorsalstream are the functional properties of the parieto-frontal circuits.For reason of space we cannot re-view here the functional properties of these circuits,e.g.that formed by area LIP and FEF,those con-stituted of parietal area VIP (ventral intraparietal)and frontal area F4 (ventral premotor cortex) or thepathway that connects area AIP (anterior intrapari-etal) with area F5 (dorsal premotor cortex).Thesame functional principle is valid,however,through-out these connections.Area F5,one of the main tar-gets of the projection from AIP (to which it sendsback recurrent connections),was thoroughly investi-gated by Rizzolatti and colleagues [30].F5 neuronscan be classiﬁed in at least two diﬀerent categories:canonical and mirror.Canonical and mirror neurons are indistinguish-able from each other on the basis of their motor re-sponses.Their visual responses,however,are quitediﬀerent.The canonical type is active in two situ-ations:(1) when grasping an object and (2) whenﬁxating that same object.For example,a neuron ac-tive when grasping a ring also ﬁres when the monkeysimply looks at the ring.This could be thought ofas a neural analogue of the “aﬀordance” of Gibson[31].The second type of neuron identiﬁed in F5,themirror neuron [6],becomes active under either of twoconditions:(1) when manipulating an object (e.g.grasping it,as for canonical neurons),and (2) whenwatching someone else performing the same actionon the same object.This is a more subtle represen-tation of objects,which allows and supports,at leastin theory,mimicry behaviours.In humans,area F5is thought to correspond to Broca’s area and thereis an intriguing link between gesture understanding,language,imitation,and mirror neurons [32,33].TheSTS region and parts of TE contain neurons that aresimilar in response to mirror neurons [34].They re-6spond to the sight of the hand;the main diﬀerencecompared to F5 is that they lack the motor response.It is likely that they participate in the processing ofthe visual information and then communicate withF5 [30],most likely via the parietal cortex.Studying the motor system is consequently a com-plete activity involving sensorimotor loops whichhave a role in the recognition of objects [35],of ac-tions [36],in planning and understanding the inten-tions of others [37] as well as in language [38,32].Theinvolvement of the motor areas during observation ofactions has been recently analyzed in human subjectsusing the H-reﬂex and TMS-evoked motor potentials[39,38].It has been shown that the so-called “motorresonance” phenomenon [30] is not relegated to thecortex but,rather,it spreads far deeper than initiallythought.It has been shown that the spinal cord ex-citability is modulated selectively under threshold bythe observation of others.In particular,in this exper-iment,the excitability of the spinal cord was assessedand it was determined to reﬂect an anticipatory pat-tern similar to the actual muscular activation withrespect to the kinematics of the action.These studies in neuroscience provided the require-ments and boundary condition for the design and im-plementation of the iCub cognitive architecture.Thisarchitecture was initially loosely modelled after the“global workspace architecture” of [40,41,42] butlater evolved into something diﬀerent which is uniqueto the iCub.This work on neuroscience was complemented byother studies in developmental psychology when cul-minated in a roadmap for the development of cog-nitive abilities in humanoid robots based on the on-togeny of human neonates.This roadmap also deﬁnesa set of scenarios and empirical tests for the iCub cog-nitive architecture.The main idea is to be able totest the iCub in the same manner as a developmen-tal psychologist would test an infant in a laboratoryexperiment.4.Speciﬁc ResultsIn this section,we summarize the main resultsto convey some of the most exciting features of theiCub.We begin by describing brieﬂy the physicaliCub platformand its software architecture before fo-cussing on sensorimotor coordination,manipulationand aﬀordances,and imitation & communication.4.1.Mechatronics of the iCubThe iCub is approximately 1m tall and weighs22kg.From the kinematic and dynamic analysis,thetotal number of degrees of freedomfor the upper bodywas set to 38 (7 for each arm,9 for each hand,and6 for the head).The hands each have three indepen-dent ﬁngers and the fourth and ﬁfth to be used foradditional stability and support (only one DOF over-all).They are tendon driven,with most of the motorslocated in the forearm.For the legs the simulationsindicated that for crawling,sitting and squatting a5 DOF leg is adequate.However,it was decided toincorporate an additional DOF at the ankle to sup-port standing and walking.Therefore each leg has6 DOF:these include 3 DOF at the hip,1 DOF atthe knee and 2 DOF at the ankle (ﬂexion/extensionand abduction/adduction).The foot twist rotationwas not implemented.Crawling simulation analy-sis also showed that for eﬀective crawling a 2 DOFwaist/torso is adequate.However,to support ma-nipulation a 3 DOF waist was incorporated.A 3DOF waist provides increased range and ﬂexibilityof motion for the upper body resulting in a largerworkspace for manipulation (e.g.when sitting).Theneck has a total of 3 DOF and provides full headmovement.The eyes have further 3 DOF to supportboth tracking and vergence behaviors.From the sensory point of view,the iCub isequipped with digital cameras,gyroscopes and ac-celerometers,microphones,and force/torque sensors.A distributed sensorized skin is under developmentusing capacitive sensors technology.Each joint is in-strumented with positional sensors,in most cases us-ing absolute position encoders.A set of DSP-basedcontrol cards,custom-designed to ﬁt the iCub,takescare of the low-level control loop in real-time.TheDSPs communicate with each other via a CAN bus.Four CAN bus lines connect the various segments ofthe robot.All sensory and motor-state informationis transferred to an embedded Pentium based PC104card that handles synchronization and reformatting7of the various data streams.Time consuming compu-tation is typically carried out externally on a clusterof machines.The communication with the robot oc-curs via a Gbit Ethernet connection.The iCub is equipped with an umbilical cord whichcontains both an Ethernet cable and power to therobot.At this stage there is no plan for making theiCub fully autonomous in terms of power supply andcomputation (e.g.by including batteries and/or ad-ditional processing power on board).Certain features of the iCub are unique.Tendondriven joints are the norm both for the hand and theshoulder,but also in the waist and ankle.This re-duces the size of the robot but introduces elasticitythat has to be considered in designing control strate-gies where high forces might be generated.The hand,for example,is fully tendon-driven (see Figure 2).Seven motors are placed remotely in the forearmandall tendons are routed through the wrist mechanism(a 2 DOF diﬀerential joint).The thumb,index,andmiddle ﬁnger are driven by a looped tendon in theproximal joint.Motion of the ﬁngers is driven bytendons routed via idle pulleys on the shafts of theconnecting joints.The ﬂexing of the ﬁngers is di-rectly controlled by the tendons while the extensionis based on a spring return mechanism.This arrange-ment saves one cable per ﬁnger.The last two ﬁngersare coupled together and pulled by a single motorwhich ﬂexes 6 joints simultaneously.Two more mo-tors,mounted directly inside the hand,are used foradduction/abduction movements of the thumb andall ﬁngers except the middle one which is ﬁxed withrespect to the palm.In summary,eight DOF outof a total of nine are allocated to the ﬁrst three ﬁn-gers,allowing considerable dexterity.The last twoﬁngers provide additional support to grasping.Jointangles are sensed using a custom-designed Hall-eﬀect-magnet pair.In addition roomfor the electronics andtactile sensors has been planned.The tactile sensorsare under development [43].The overall size of thepalm has been restricted to 50mm in length;it is34mm wide at the wrist and 60mm at the ﬁngers.The hand is only 25mm thick.Figure 2:The hand of the iCub,showing some of the tendons,the sensorized ﬁngertips and the coating of the sensors of thepalm (108 taxels overall).Tendons are made of Teﬂon-coatedcables sliding inside Teﬂon coated ﬂexible steel tubes.4.2.Software ArchitectureConsiderable eﬀort went into the development ofa suitable software infrastructure.The iCub soft-ware was developed on top of YARP [44].TheiCub project supported a major overhaul of theYARP libraries to adapt to a more demanding col-laborative environment.Better engineered softwareand interface deﬁnitions are now available.YARPis a set of libraries that supports modularity by ab-stracting two common diﬃculties in robotics:namely,modularity in algorithms and in interfacing with thehardware.Robotics is perhaps one of the most de-manding application environments for software recy-cling where hardware changes often,diﬀerent spe-cialized OSs are typically encountered in a contextwith a strong demand for eﬃciency.The YARP li-braries assume that an appropriate real-time layeris in charge of the low-level control of the robot andinstead takes care of deﬁning a soft real-time commu-nication layer and hardware interface that is suitedfor cluster computation.YARP takes care also ofproviding independence from the operating system8and the development environment.The main tools inthis respect are ACE [45] and CMake.The former isan OS-independent communication library that hidesthe quirks of interprocess communication across dif-ferent OSs.CMake is a cross-platform make-like de-scription language and tool to generate appropriateplatform speciﬁc project ﬁles.YARP abstractions are deﬁned in terms of pro-tocols.The main YARP protocol addresses inter-process communication issues.The abstraction isimplemented by the Port C++ class.Ports followthe observer pattern by decoupling producers andconsumers.They can deliver messages of any size,across a network using a number of underlying pro-tocols (including shared memory when possible).Indoing so,Ports decouple as much as possible (as func-tion of a certain number of user-deﬁned parameters)the behavior of the two sides of the communicationchannels.Ports can be commanded at run time toconnect and disconnect.The second abstraction of YARP concerns hard-ware devices.The YARP approach is to deﬁne in-terfaces for classes of devices to wrap native codeAPIs (often provided by the hardware manufactures).Change in hardware will likely require only a changein the API calls (and linking against the appropriatelibrary).This easily encapsulates hardware depen-dencies but leaves dependencies in the source code.The latter can be removed by providing a “factory”for creating objects at run time (on demand).Thecombination of the port and device abstractions leadsto remotable device drivers which can be accessesacross a network:e.g.a grabber can send imagesto a multitude of listeners for parallel processing.Overall,YARP’s philosophy is to be lightweightand to be “gentle” with existing approaches and li-braries.This naturally excludes hard real-time issuesthat have to be necessarily addressed elsewhere,likelyat the OS level.4.3.Sensorimotor Coordination ModelsThe iCub’s cognitive capabilities depend greatlyon the development of sensorimotor coordination andsensorimotor mapping.At the outset,we identiﬁedhow the sensorimotor system is determined by biol-ogy,how this is expressed in development,and howexperience enters into the process in forming reliableand sophisticated tools for exploring and manipulat-ing the outside world.Our particular concern hereis to identify the sensory information (visual,pro-prioceptive,auditory) that is necessary to organizegoal-directed actions.As with everything else,theseissues are ﬁrst investigated in humans and then usedto deﬁne the iCub’s cognitive architecture.The re-search on sensorimotor coordination has two distinctthemes.1.Modelling how sensorimotor systems evolve fromsets of relatively independent mechanisms to uni-ﬁed functional systems.In particular,we studyand model the ontogenesis of looking and reach-ing,for example by asking the following ques-tions:how does gaze control evolve from thesaccadic behaviour of newborns to the preciseand dynamic mode of control that takes intoaccount both the movement of the actor andthe motion of objects in the surrounding?Howdoes reaching evolve from the crude coordina-tion in newborns to the sophisticated and skil-ful manipulation in older children?In addition,we model how diﬀerent sensorimotor maps (forgaze/head orienting,for reaching,for grasping,etc.) can be fused to form a subjectively uni-tary perception/action system.We look also athow the brain coordinates diﬀerent eﬀectors toform a “pragmatic” representation of the exter-nal world using neurophysiological,psychophys-ical,and robotics techniques.2.Modelling the role of motor representation astools serving not only action but also percep-tion.This topic,on which we will expand laterin the paper,clearly beneﬁts from a unifying vi-sion based on the idea that the motor system(atleast at its representational level) forms the “ac-tive ﬁlter” carving out passively perceived stim-uli by means of attentional or “active percep-tion” processes.The postulate that action and perception are inter-woven with each other and form the basis of highercognition is in contrast with the established modularview according to which perceptually-related activ-ity in motor systems could still be accounted for in9the sense of bottom-up eﬀects.As the importanceof sensory input on the control of actions is widelyagreed upon,an evaluation of,and,eventually,deci-sion between,the two alternative positions criticallydepends on the question whether activity in motorsystems is relevant for perception and comprehen-sion.In summary,along these lines we realized a layeredcontroller system for the iCub including:1.Spinal behaviours:e.g.rhythmic movement andbasic synergies,force feedback.We developed anarchitecture for the generation of discrete andrhythmic movements where trajectories can bemodulated by high-level commands and sensoryfeedback [46].2.Eye movements and attention:an attention sys-tem was developed which includes sensory inputprocessing (vision and audition),eye-neck coor-dination,eye movements (smooth pursuit,sac-cades,VOR and vergence).Methods for track-ing behind occlusions have been also investigated[47].3.Reaching and body schemas:a robust task-space reaching controller has been developedand methods for learning internal models tested.Speciﬁcally,generic inverse kinematics modelsand human-like trajectory generation has beenimplemented for the iCub by taking into accountvarious constraints such as joint limits,obstacles,redundancy and singularities [48].4.Grasping:ﬁnally,based on reaching and orient-ing behaviours,a grasping module has been im-plemented.This allows the coordination of look-ing (for a potential target),reaching for it (plac-ing the hand close to the target) and attemptinga grasping motion (or another basic action).The investigation from a neuroscientiﬁc perspec-tive of sensorimotor representations and their role incognitive functions contributed directly to the imple-mentation of sensorimotor skills in the iCub basedon a biologically plausible model for object interac-tion and the recognition of actions in others.Manyexperimental techniques and approaches have beenused to pursue this goal.In particular,we con-ducted electrophysiological experiments on both hu-mans and animals (transcranial magnetic stimula-tion,single neuron recordings),brain imaging ex-periments (functional magnetic resonance,near in-frared spectroscopy),kinematics and gaze trackingrecordings,behavioural experiments on both nor-mal individuals and patients (autistic children andfrontal aphasic patients).These contributions servedto clarify the strict interdependence between the mo-tor command and the sensory consequences of actionexecution and its fundamental role in the buildingand development of cognitive functions.For example,functional brain studies showed thatthe human mirror system responds similarly to theprimate mirror neuron system,and relies on an in-ferior frontal,premotor,and inferior parietal corticalnetwork.Furthermore,this mirror systemis more ac-tivated when subjects observe movements for whichthey have developed a speciﬁc competence or whenthey listen to rehearsed musical pieces compared withmusic they had never played before.Though humansrely greatly on vision,individuals who lack sight sincebirth still retain the ability to learn actions and be-haviours from others.To what extent is this abilitydependent on visual experience?Is the human mir-ror system capable of interpreting nonvisual infor-mation to acquire knowledge about others?It turnsout that the mirror system is also recruited when in-dividuals receive suﬃcient clues to understand themeaning of the occurring action with no access to vi-sual features,such as when they only listen to thesound of actions or to action-related sentences.Inaddition,neural activity in the mirror system whilelistening to action sounds is suﬃcient to discrimi-nate which of two actions another individual has per-formed.Thus,while these ﬁndings suggest that mir-ror system may be activated also by hearing,they donot rule out that its recruitment may be the conse-quence of a sound-elicited mental representation ofactions through visually-based motor imagery.We used functional magnetic resonance imaging(fMRI) to address the role of visual experience onthe functional development of the human mirror sys-tem.Speciﬁcally,we determined whether an eﬃcientmirror system also develops in individuals who havenever had any visual experience.We hypothesized10that mirror areas that further process visually per-ceived information of others’ actions and intentionsare capable of processing the same information ac-quired through nonvisual sensory modalities,such ashearing.Additionally,we hypothesized that individ-uals would show a stronger response to those actionsounds that are part of their motor repertoire.To this purpose,we used an fMRI sparse sam-pling six-run block design to examine neural activityin blind and sighted healthy volunteers while theyalternated between auditory presentation of hand-executed actions (e.g.,cutting paper with scissors)or environmental sounds (e.g.,rainstorm),and ex-ecution of a “virtual” tool or object manipulationtask (motor pantomime).Results show that incongenitally blind individuals,aural presentation offamiliar actions compared with the environmentalsounds elicited patterns of neural activation involv-ing premotor,temporal,and parietal cortex,mostlyin the left hemisphere,similar to those observed insighted subjects during both aural and visual pre-sentation.These ﬁndings demonstrate that a leftpremotortemporo-parietal network subserves actionperception through hearing in blind individuals whohave never had any visual experience,and that thisnetwork overlaps with the left-lateralized mirror sys-tem network that was activated by visual and audi-tory stimuli in the sighted group.Thus,the mirrorsystem can develop in the absence of sight and canprocess information about actions that is not visual.Further,the results in congenitally blind individu-als unequivocally demonstrate that the sound of anaction engages human mirror system brain areas foraction schemas that have not been learned throughthe visual modality.Along the same line of investigation,we askedwhether other people’s actions are understood by pro-jecting them onto one’s own action programs andwhether this mode of control functions in infants.The gaze and hand movements of both adults and in-fants were measured in two live situations.The taskwas either to move an object between two places inthe visual ﬁeld or to observe the corresponding ac-tion performed by another person.When the sub-jects performed the action,infants and adults be-haved strikingly similar.They initiated the hand andgaze movements simultaneously and gaze arrived atthe goal ahead of the hand.When observing suchactions,the initiation of the gaze shifts was delayedrelative to the observed movement in both infantsand adults but gaze still arrived at the goal aheadof the hand.The infants’ gaze shifts,however,weremore delayed at the start,less proactive at the goal,and showed kinematic variability indicating that thismode of functioning is somewhat unstable in 10-month-old infants.In summary,the results showedthat both adults and infants perceive the goal of theaction and move gaze there ahead of time,but theydid not support the idea of a strict matching of thekinematics between the eye movements carried outwhen performing and observing actions.4.4.Object AﬀordancesThe term aﬀordance was originally used by JamesJ.Gibson to refer to all “action possibilities” on acertain object,with reference to the actor’s capabil-ities.Thus,a chair is only “sit-able” for a perceiverof a certain height.However,whether an aﬀordanceis exploited by a perceiver or not has to do with thegoals,values,and interests of this perceiver.Building on the sensorimotor coordination,theiCub can also develop the ability to learn the aﬀor-dances of objects.Speciﬁc models of how the pri-mates brain represents aﬀordances were considered(for example the parietal-frontal circuit) as well asresults from psychological sciences.Speciﬁcally,weinvestigated what exploratory behaviours support theacquisition of aﬀordances and what is the relevant in-formation (visual,haptic,motor,etc.).We developeda model of the acquisition of object aﬀordances andhowthe motor information enters into the descriptionof perceptual quantities.In analogy to what observedis in the brain,we also investigated how the deﬁnitionof purpose (or goal) participates in the representationof the actions an object aﬀords.Humans learn to exploit object aﬀordancesthroughout their entire life but not all are learnt au-tonomously.A large set is conveyed by social meanseither by communication or by observing others ac-tions.Due to the complexity of the human develop-mental process,it is diﬃcult to separate the impor-tance of learning by exploration and learning from11others.Furthermore,learning fromothers may some-times just be a question of highlighting a certain af-fordance.Notwithstanding this,we distinguish twomeans of acquisition of object aﬀordances:that is,self-exploration (autonomous learning) and by obser-vation (learning from examples).From a develop-mental perspective,it is natural to consider that self-exploration precedes the observation stage,thoughthey are not simply sequential stages.Learning byobservation requires some minimal capabilities,suchas object and action recognition,in order to inferother agents’ actions on objects,which are capabil-ities acquired by previous self-interaction with theenvironment.Therefore,for learning aﬀordances,itis essential to be able to locate objects in the en-vironment and execute goal-directed motor actionsover objects.Much of the work on sensorimotor co-ordination focuses on the development of capabilitiesfor controlling one’s own actions which constitutes animportant part of the primitives for the acquisitionof object aﬀordances.After the system has acquiredthe capability to coordinate movements with respectto sensory information,it can start interacting withobjects and understanding its interface — how tograb the object,what are the eﬀects of certain ap-plied actions.Then,the systemmay start recognizingand interpreting other agents interacting with similarobjects,learning other object aﬀordances and inter-preting activities.These capabilities have importantrelationship with the development of imitation andgesture communication (to be described below).For learning aﬀordances,we use Bayesian Networks(BN) to model the dependencies between robot ac-tions,object characteristics,and the resulting eﬀects[49].Brieﬂy,a BN is described by a set of nodesthat represent randomvariables,a set of directed arcsthat encode conditional dependencies and a set ofconditional probability distributions.A BN encodescausality since an arc froma node X to a node Y canbe interpreted as X causes Y.We assumed that theiCub has developed certain skills prior to be able tolearn aﬀordance (as described in section 4.3):a mo-tor repertoire (A),perhaps derived from experience,an object feature repertoire (F) also potentially ac-quired via object manipulation and the eﬀects (E)resulting from manipulating the environment.(a) (b)Figure 3:(a) General aﬀordance scheme relating actions,ob-jects (through their characteristics) and the resulting eﬀects.(b) A particular BN encoding aﬀordances.InputsOutputsFunction(O,A)EPredict eﬀect(O,E)ARecognize action & planning(A,E)OObject recognition & selectionsTable 1:Using aﬀordances for prediction,recognition,andplanning.The interaction of the iCub with the environmentis therefore formalized in using one action a from Aon certain objects with features F (or a subset ofthem) to obtain eﬀects e from E.This informationcan be used to estimate the BN structure and pa-rameters using diﬀerent learning algorithms.Theseparameters can be updated online as the robot per-forms more experiments.Also,they can be updatedby observation of other agents.Examples are shownin Figure 3.This model has some nice properties;for example,aﬀordances can be learned autonomously by experi-ence and by self-observation,restricting the updateof the probability distributions.Features can be ei-ther selected or ignored,depending on their salience,and the model can be used to perform prediction,recognition,and planning,depending on how the af-fordance network is traversed.This traversal is basedon probabilistic queries.These queries may take asinput any combination of actions,objects and fea-tures and compute conditional distributions of oneor more of the other variables.Table 1 summarizes12some of the basic operations that can be performedwith the network.Based on this previous model,we have performedseveral experiments with the robotic platform shownin Figure 4.We used a playground scenario consist-ing of several objects with two shapes (box and ball),diﬀerent sizes and colours.The iCub was able toperformthree diﬀerent actions:grasp,tap and touch.An example of an aﬀordance network is shown in Fig-ure 5.These results show how the model is able tocapture the basic object behaviour under diﬀerent ac-tions.For instance,colour is irrelevant in our setup.The shape has an eﬀect on the object velocity (OV )and distance (Di) since tapping a ball or a box resultsin diﬀerent eﬀects (boxes do not roll).As expected,the hand velocity (HV ) only depends on the selectedaction.The object hand distance (Di) also dependson the size since very big objects cannot be graspedby the robot.It is important to note that these rela-tions are shaped by the experience of the robot andby its current skills.Another important property isthat the detection of object features and eﬀects is notperfect and the systemhas to cope with errors.In thesame way,the same action on the same object doesnot always produce the same results.The probabilis-tic representation inherent to BN allows capturingand coping with this uncertainty.4.5.Imitation and CommunicationProgress has also been made in integrating imi-tation and communicationin an ontogenetic frame-work on the iCub platform.Imitation plays a centralrole and communication is strongly related to imita-tion as regards social cues,turn-taking,and commu-nicative functions.Our particular concern here arethe cognitive skills required for imitative behavioursand the cognitive skills required for communicatingthrough body gestures.We also investigated the reg-ulation of interaction dynamics of social interactionduring human-robot play and its development in on-togeny.The pre-requisites for interactive and com-municative behaviour grounded in sensorimotor ex-perience and interaction histories were investigatedand developed with speciﬁc consideration of interac-tion kinesics (including gestures,synchronization andFigure 4:The playground for the robot contains objects of sev-eral sizes,colours and shapes.Protocol:the object to interactwith is selected manually,the action is random (from the setof actions A).Object properties are recorded when the handis not occluding the object.The eﬀects are recorded later andthen the robot hand goes open loop to a resting position.rhythms of movements etc.).Social drives for inter-action,imitation and communication were consideredto make use of non-verbal social cues in ontogeny inthe course of human-robot interaction.This work relies on fairly sophisticated cognitiveskills which include the ability to recognize and in-terpret somebody else’s gestures in terms of its owncapabilities (mirror eﬀects),the ability to learn newgestures on the basis of the observation of those inother individuals,and the ability to recognize thepurpose of other people’s gestures,such as the goal ofmanipulating objects in a certain speciﬁc way.It alsorelies on the ability to predict the result of a demon-strated manipulation task and to use this ability todiscriminate between good and poor demonstrationsof manipulation tasks based on their aﬀordances.Fi-nally,the ability to decide what part of the demon-stration is relevant to imitation is required.Prerequisites to these skills are the skilful controlof arms and body in order to produce communica-tive gestures reﬂecting communicative timing or turn-taking,tracking and recognizing someone elses gestu-ral timing,synchrony,and social engagement,to gen-13Figure 5:Learned network.The variables represent A Action,C Object Colour,Sh Object Shape,S Object Size,OV Objectvelocity proﬁle,HV Hand velocity proﬁle,Di Hand objectdistance proﬁle.eralize and acquire simple communicative behavioursmaking use of social cues,to respond adequately totiming and gesturing of an interaction partner,andto harness turn taking as the underlying rhythm ofgestured communication.That is,both the staticaspect of recognition of actions and their social andtemporal qualities have to be mastered before properimitation and communication can happen.A large part of this iCub work took a human-robotinteraction perspective to analyzing and developingcontrollers to enhance human-robot communication.This work addressed the above delineated goals ofdetermining the role that timing,social cues,andgesture recognition play in human-robot communica-tion.Further,progress on the development of algo-rithms for imitation learning was made by extendingwork on statistical estimate of motion dynamics toallow robust estimation of arbitrary non-linear au-tonomous dynamical systems.A number of human studies on various topics per-taining to the basis of human-human communicationand imitation were also conducted.These studiesfocused on the observation-action/perception-actionloop for both basic motor task and high-level cog-nitive tasks,such as speech production and percep-tion.In addition,the project conducted a user-studyto delineate the variables controlled during imitationof simple goal-directed arm reaching motion.Thisstudy informed the development of a computationalmodel of reaching movement that uses the same non-linear dynamical formas that used in the robotics im-itation work mentioned above.Further experimentswere directed at determining the role of Broca’s areain the perception of various types of events (biolog-ical vs.non-biological) but also on the involvementof the motor system in the perception of speech andin inter-personal interaction under the inﬂuence of areward.In particular,one quite fundamental experiment[38] has shown that listening to speech recruits a net-work of fronto-temporoparietal cortical areas.Clas-sical models consider anterior (motor) sites to be in-volved in speech production whereas posterior sitesare considered to be involved in comprehension.Thisfunctional segregation is challenged by action percep-tion theories suggesting that brain circuits for speecharticulation and speech perception are functionallydependent.Although recent data show that speechlistening elicits motor activities analogous to produc-tion,it’s still debated whether motor circuits play acausal contribution to the perception of speech.Here,we set out to investigate the functional con-tributions of the motor-articulatory systems to spe-ciﬁc speech-perception processes.To this end,across-over design orthogonalizing the eﬀect of brain-phonology concordance with those of linguistic stim-uli and TMS loci was chosen.Phonemes producedwith diﬀerent articulators (lip-related:[b] and [p];tongue-related:[d] and [t]) were presented in aphoneme discrimination task.The eﬀect of TMS tolip and tongue representations in precentral cortex,as previously described by fMRI,was investigated.Double TMS pulses were applied just prior to stimulipresentation to selectively prime the cortical activityspeciﬁcally in the lip (LipsM1) or tongue (TongueM1)area.Behavioural eﬀects were measured via reactiontimes and error rates.Reaction time performance showed a behaviouraldouble dissociation between stimulation site andstimulus categories.Reaction time change of phono-logical decisions induced by TMS pulses to eitherthe TongueM1 or LipM1 showed opposite eﬀects fortongue- and lip-produced sounds.Therefore,thestimulation of a given M1 representation led to bet-ter performance in recognizing speech sounds pro-14duced with the concordant eﬀector compared withdiscordant sounds produced with a diﬀerent eﬀector.These results provide strong support for a speciﬁcfunctional role of motor cortex in the perception ofspeech sounds.In parallel,we tested whether TMSwas able to modulate the direction of errors.Errorswere grouped in two classes:lip-phoneme errors (L-Ph-miss) and tongue-phoneme errors (T-Ph-miss).The double dissociation we found in the presentwork provides evidence that motor cortex contributesspeciﬁcally to speech perception.As shown by bothRTs and errors,the perception of a given speechsound was facilitated by magnetically stimulating themotor representation controlling the articulator pro-ducing that sound,just before the auditory presenta-tion.Biologically grounded models of speech and lan-guage have previously postulated a functional link be-tween motor and perceptual representations of speechsounds.We demonstrate here for the ﬁrst time a spe-ciﬁc causal link for features of speech sounds.Therelevant areas in motor cortex seem to be also rele-vant for controlling the tongue and lips,respectively.5.ConclusionTo the best of our knowledge,the iCub cogni-tive humanoid robot is at the forefront of researchin developmental robotics.The iCub was designedcompletely from scratch — mechanics,electronics,ﬁrmware,and software — speciﬁcally with the re-quirements of developmental cognition in mind.Itsdesign is based on a roadmap of human development[1] which already contains a full-ﬂedged program ofempirical research that may keep scientists busy formany years to come.This description of human de-velopment stresses the role of prediction into the skil-ful control of movement:development is in a sensethe gradual maturation of predictive capabilities.Itincorporates a model of sensorimotor control anddevelopment which considers action (that is,move-ments with a goal,generated by a motivated agentwhich are predictive in nature) as the basic elementof cognitive behaviours.Experiments with infantsand adults have shown that the brain is not made ofa set of isolated areas dealing with perception or mo-tor control but rather that multisensory neurons arethe norm.Experiments have proven the involvementof the motor system in the ﬁne perception of othersmovements including speech.The iCub uses a com-putational model of aﬀordances which includes thepossibility of learning both the structure of depen-dences between sets of random variables (e.g.per-ceptual qualities vs.action and results),their eﬀec-tive links and their use in deciding how to controlthe robot.Aﬀordances are the quintessential prim-itives of cognition by mixing perception and actionin a single concept (representation);this represen-tation has facilitated the creation of a computationmodel of imitation and interaction between humansand robots by evaluating the automatic construc-tion of models from experience (e.g.trajectories),their correction via feedback,timing and synchro-nization.This explores the domain between meresensorimotor associations and the possibility of truecommunication between robot and people.Finally,the iCub project has given rise to a large and grow-ing community of highly-active users,developers,andresearchers drawn frommany disciplines,all commit-ted to creating the iCub of the future.Although much is still to be done to implementthe cognitive skills described in our roadmap of hu-man development [1],we believe the iCub to be amilestone in cognitive systems research by providinga solid framework for the community at large and forthe ﬁrst time providing opportunities for widespreadcollaborative progress.This is possible because of theopportunity of creating critical mass,using a com-mon robotic platformand common software architec-ture,with the availability of technical support froman enthusiastic multidisciplinary team of developers,researchers and cognitive scientists.This places theiCub at the forefront of research in cognitive systemsand robotics and fosters truly international collabo-ration by its adoption of the Open Source model.AcknowledgementsThis work was supported by the EuropeanCommission,Project IST-004370 RobotCub,underStrategic Objective 2.3.2.4:Cognitive Systems.15References[1] C.von Hofsten,L.Fadiga,D.Vernon,ARoadmap for the Development of Cognitive Ca-pabilities in Humanoid Robots,in press,2010.[2] G.Metta,P.Fitzpatrick,L.Natale,Yarp:yetanother robot platform,International Journal onAdvanced Robotics Systems 3 (1) (2006) 43–48.[3] D.Vernon,G.Metta,G.Sandini,A survey ofartiﬁcial cognitive systems:Implications for theautonomous development of mental capabilitiesin computational agents,IEEE Transaction onEvolutionary Computation 11 (2) (2007) 151–180.[4] 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